test_that("update_mallows works", { set.seed(1) data_first_batch <- potato_visual[1:4, ] mod_init <- compute_mallows( data = setup_rank_data(data_first_batch), compute_options = set_compute_options(nmc = 100, burnin = 0) ) data_second_batch <- potato_visual[5:8, ] mod_second <- update_mallows( model = mod_init, new_data = setup_rank_data(rankings = data_second_batch), smc_options = set_smc_options(n_particles = 10) ) pi <- compute_posterior_intervals(mod_second) expect_equal(pi$hpdi, "[0.331,0.771]") expect_equal(mod_second$alpha$value[[9]], 0.528698473629568) data_third_batch <- potato_visual[9:12, ] mod_final <- update_mallows( model = mod_second, new_data = setup_rank_data(rankings = data_third_batch) ) expect_equal(mod_final$rho$value[169], 17) # Need this because random numbers don't reproduce exactly on M1 skip_on_os("mac", arch = "aarch64") potato_top_10 <- ifelse(potato_visual[1:10, ] > 10, NA_real_, potato_visual[1:10, ] ) potato_top_12 <- ifelse(potato_visual[1:10, ] > 12, NA_real_, potato_visual[1:10, ] ) potato_top_14 <- ifelse(potato_visual[1:10, ] > 14, NA_real_, potato_visual[1:10, ] ) user_ids <- rownames(potato_visual[1:10, ]) mod_init <- compute_mallows( data = setup_rank_data(rankings = potato_top_10, user_ids = user_ids), compute_options = set_compute_options(nmc = 100, burnin = 5) ) mod1 <- update_mallows( model = mod_init, new_data = setup_rank_data(rankings = potato_top_12, user_ids = user_ids), smc_options = set_smc_options(n_particles = 20), compute_options = set_compute_options(aug_method = "pseudo") ) expect_equal(mod1$alpha$value[[13]], 0.544567207683911) mod2 <- update_mallows( model = mod1, new_data = setup_rank_data(rankings = potato_top_14, user_ids = user_ids) ) expect_equal(mod2$rho$value[[300]], 2) potato_new <- potato_visual[11:12, ] user_ids <- rownames(potato_new) mod_final <- update_mallows( model = mod2, new_data = setup_rank_data(rankings = potato_new, user_ids = user_ids) ) expect_equal(mod_final$rho$value[[300]], 3) }) test_that("update_mallows handles estimated partition function", { set.seed(199) dat <- t(replicate(3, sample(22))) fit <- estimate_partition_function( method = "asymptotic", alpha_vector = seq(from = 0, to = 1, by = .1), n_items = 22, metric = "spearman", n_iterations = 50 ) mod <- compute_mallows( data = setup_rank_data(dat), model_options = set_model_options(metric = "spearman"), compute_options = set_compute_options(nmc = 10, burnin = 0), pfun_estimate = fit ) expect_equal(mod$pfun_estimate, fit) newdat <- t(replicate(3, sample(22))) mod1 <- update_mallows( model = mod, new_data = setup_rank_data(newdat), model_options = set_model_options(metric = "spearman"), smc_options = set_smc_options(n_particles = 5) ) expect_equal(mod1$pfun_estimate, fit) newdat <- t(replicate(3, sample(22))) mod2 <- update_mallows( model = mod1, new_data = setup_rank_data(newdat) ) expect_equal(mod2$pfun_estimate, fit) })